Overview

Dataset statistics

Number of variables12
Number of observations364
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.2 KiB
Average record size in memory96.4 B

Variable types

Numeric11
Categorical1

Warnings

df_index is uniformly distributed Uniform
df_index has unique values Unique

Reproduction

Analysis started2021-04-27 05:44:29.583248
Analysis finished2021-04-27 05:46:35.724246
Duration2 minutes and 6.14 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct364
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182.5
Minimum1
Maximum364
Zeros0
Zeros (%)0.0%
Memory size3.0 KiB
2021-04-26T23:46:35.852518image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19.15
Q191.75
median182.5
Q3273.25
95-th percentile345.85
Maximum364
Range363
Interquartile range (IQR)181.5

Descriptive statistics

Standard deviation105.2219876
Coefficient of variation (CV)0.576558836
Kurtosis-1.2
Mean182.5
Median Absolute Deviation (MAD)91
Skewness0
Sum66430
Variance11071.66667
MonotocityStrictly increasing
2021-04-26T23:46:36.056588image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.3%
2511
 
0.3%
2491
 
0.3%
2481
 
0.3%
2471
 
0.3%
2461
 
0.3%
2451
 
0.3%
2441
 
0.3%
2431
 
0.3%
2421
 
0.3%
Other values (354)354
97.3%
ValueCountFrequency (%)
11
0.3%
21
0.3%
31
0.3%
41
0.3%
51
0.3%
ValueCountFrequency (%)
3641
0.3%
3631
0.3%
3621
0.3%
3611
0.3%
3601
0.3%

Age
Real number (ℝ≥0)

Distinct73
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.91758242
Minimum11
Maximum89
Zeros0
Zeros (%)0.0%
Memory size3.0 KiB
2021-04-26T23:46:36.231152image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile16
Q128
median44.5
Q360
95-th percentile75.85
Maximum89
Range78
Interquartile range (IQR)32

Descriptive statistics

Standard deviation18.78085358
Coefficient of variation (CV)0.4181180859
Kurtosis-1.011961372
Mean44.91758242
Median Absolute Deviation (MAD)15.5
Skewness0.1470426138
Sum16350
Variance352.7204614
MonotocityNot monotonic
2021-04-26T23:46:36.403350image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6518
 
4.9%
3516
 
4.4%
5715
 
4.1%
6013
 
3.6%
2813
 
3.6%
2712
 
3.3%
2411
 
3.0%
5511
 
3.0%
3010
 
2.7%
3810
 
2.7%
Other values (63)235
64.6%
ValueCountFrequency (%)
111
 
0.3%
121
 
0.3%
135
1.4%
147
1.9%
153
0.8%
ValueCountFrequency (%)
891
 
0.3%
871
 
0.3%
832
 
0.5%
812
 
0.5%
806
1.6%

Sex
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
0.0
203 
1.0
161 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1092
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0
ValueCountFrequency (%)
0.0203
55.8%
1.0161
44.2%
2021-04-26T23:46:36.674643image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-26T23:46:36.748832image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0203
55.8%
1.0161
44.2%

Most occurring characters

ValueCountFrequency (%)
0567
51.9%
.364
33.3%
1161
 
14.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number728
66.7%
Other Punctuation364
33.3%

Most frequent character per category

ValueCountFrequency (%)
0567
77.9%
1161
 
22.1%
ValueCountFrequency (%)
.364
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1092
100.0%

Most frequent character per script

ValueCountFrequency (%)
0567
51.9%
.364
33.3%
1161
 
14.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1092
100.0%

Most frequent character per block

ValueCountFrequency (%)
0567
51.9%
.364
33.3%
1161
 
14.7%

PCV
Real number (ℝ≥0)

Distinct188
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.76291209
Minimum13.1
Maximum56.9
Zeros0
Zeros (%)0.0%
Memory size3.0 KiB
2021-04-26T23:46:36.849935image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum13.1
5-th percentile25.015
Q132.9
median36.8
Q341.85
95-th percentile47.285
Maximum56.9
Range43.8
Interquartile range (IQR)8.95

Descriptive statistics

Standard deviation6.830834586
Coefficient of variation (CV)0.1858077665
Kurtosis0.4699496764
Mean36.76291209
Median Absolute Deviation (MAD)4.6
Skewness-0.3661237376
Sum13381.7
Variance46.66030114
MonotocityNot monotonic
2021-04-26T23:46:36.974467image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.17
 
1.9%
32.96
 
1.6%
36.85
 
1.4%
33.25
 
1.4%
355
 
1.4%
27.44
 
1.1%
39.74
 
1.1%
42.34
 
1.1%
43.24
 
1.1%
34.84
 
1.1%
Other values (178)316
86.8%
ValueCountFrequency (%)
13.12
0.5%
16.61
0.3%
16.71
0.3%
17.51
0.3%
201
0.3%
ValueCountFrequency (%)
56.91
 
0.3%
52.21
 
0.3%
51.71
 
0.3%
501
 
0.3%
49.63
0.8%

MCV
Real number (ℝ≥0)

Distinct200
Distinct (%)54.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.50912088
Minimum55.7
Maximum124.1
Zeros0
Zeros (%)0.0%
Memory size3.0 KiB
2021-04-26T23:46:37.101257image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum55.7
5-th percentile73.315
Q183.175
median87.95
Q391.875
95-th percentile102.135
Maximum124.1
Range68.4
Interquartile range (IQR)8.7

Descriptive statistics

Standard deviation9.332164102
Coefficient of variation (CV)0.1066421878
Kurtosis2.663400254
Mean87.50912088
Median Absolute Deviation (MAD)4.45
Skewness0.1382931144
Sum31853.32
Variance87.08928683
MonotocityNot monotonic
2021-04-26T23:46:37.275078image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89.29
 
2.5%
88.17
 
1.9%
87.87
 
1.9%
94.95
 
1.4%
85.65
 
1.4%
88.94
 
1.1%
864
 
1.1%
90.14
 
1.1%
91.34
 
1.1%
90.44
 
1.1%
Other values (190)311
85.4%
ValueCountFrequency (%)
55.71
0.3%
57.31
0.3%
58.51
0.3%
60.11
0.3%
62.11
0.3%
ValueCountFrequency (%)
124.11
0.3%
122.11
0.3%
1192
0.5%
117.31
0.3%
113.81
0.3%

MCH
Real number (ℝ≥0)

Distinct140
Distinct (%)38.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.22714286
Minimum14.7
Maximum41.4
Zeros0
Zeros (%)0.0%
Memory size3.0 KiB
2021-04-26T23:46:37.441140image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum14.7
5-th percentile21.715
Q126.3
median28.2
Q330.4
95-th percentile34.2
Maximum41.4
Range26.7
Interquartile range (IQR)4.1

Descriptive statistics

Standard deviation3.865998148
Coefficient of variation (CV)0.1369603069
Kurtosis1.647355844
Mean28.22714286
Median Absolute Deviation (MAD)2
Skewness0.02165163326
Sum10274.68
Variance14.94594168
MonotocityNot monotonic
2021-04-26T23:46:39.943188image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.812
 
3.3%
279
 
2.5%
29.97
 
1.9%
30.47
 
1.9%
27.67
 
1.9%
26.77
 
1.9%
26.86
 
1.6%
27.26
 
1.6%
28.46
 
1.6%
27.36
 
1.6%
Other values (130)291
79.9%
ValueCountFrequency (%)
14.71
0.3%
15.81
0.3%
171
0.3%
17.72
0.5%
18.61
0.3%
ValueCountFrequency (%)
41.41
0.3%
41.22
0.5%
40.11
0.3%
39.11
0.3%
38.21
0.3%

MCHC
Real number (ℝ≥0)

Distinct102
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.05340659
Minimum23.6
Maximum50.2
Zeros0
Zeros (%)0.0%
Memory size3.0 KiB
2021-04-26T23:46:40.104965image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum23.6
5-th percentile28.7
Q130.3
median31.7
Q333.3
95-th percentile38
Maximum50.2
Range26.6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.801790443
Coefficient of variation (CV)0.08741006778
Kurtosis5.441354479
Mean32.05340659
Median Absolute Deviation (MAD)1.5
Skewness1.399070172
Sum11667.44
Variance7.850029685
MonotocityNot monotonic
2021-04-26T23:46:40.314982image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.310
 
2.7%
32.410
 
2.7%
31.39
 
2.5%
33.99
 
2.5%
32.19
 
2.5%
30.28
 
2.2%
318
 
2.2%
30.67
 
1.9%
31.27
 
1.9%
32.67
 
1.9%
Other values (92)280
76.9%
ValueCountFrequency (%)
23.61
0.3%
25.51
0.3%
26.31
0.3%
26.41
0.3%
26.71
0.3%
ValueCountFrequency (%)
50.21
0.3%
421
0.3%
411
0.3%
40.21
0.3%
39.61
0.3%

RDW
Real number (ℝ≥0)

Distinct83
Distinct (%)22.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.11651099
Minimum10.6
Maximum29.2
Zeros0
Zeros (%)0.0%
Memory size3.0 KiB
2021-04-26T23:46:40.504123image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum10.6
5-th percentile12.7
Q113.6
median14.8
Q316.1
95-th percentile18.585
Maximum29.2
Range18.6
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation2.176557337
Coefficient of variation (CV)0.1439854302
Kurtosis7.457132928
Mean15.11651099
Median Absolute Deviation (MAD)1.2
Skewness1.960530992
Sum5502.41
Variance4.737401843
MonotocityNot monotonic
2021-04-26T23:46:40.678732image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.112
 
3.3%
13.311
 
3.0%
16.111
 
3.0%
13.611
 
3.0%
14.210
 
2.7%
13.910
 
2.7%
12.910
 
2.7%
14.910
 
2.7%
14.710
 
2.7%
14.69
 
2.5%
Other values (73)260
71.4%
ValueCountFrequency (%)
10.61
0.3%
11.31
0.3%
11.41
0.3%
11.81
0.3%
12.11
0.3%
ValueCountFrequency (%)
29.21
0.3%
252
0.5%
24.61
0.3%
22.51
0.3%
21.32
0.5%

TLC
Real number (ℝ≥0)

Distinct237
Distinct (%)65.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.863571429
Minimum2
Maximum42.42
Zeros0
Zeros (%)0.0%
Memory size3.0 KiB
2021-04-26T23:46:40.872865image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.2
Q15.9
median7.935
Q310.205
95-th percentile16.8625
Maximum42.42
Range40.42
Interquartile range (IQR)4.305

Descriptive statistics

Standard deviation4.868502227
Coefficient of variation (CV)0.5492709418
Kurtosis14.0015884
Mean8.863571429
Median Absolute Deviation (MAD)2.065
Skewness2.941297674
Sum3226.34
Variance23.70231393
MonotocityNot monotonic
2021-04-26T23:46:41.054315image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.46
 
1.6%
7.65
 
1.4%
4.55
 
1.4%
6.35
 
1.4%
9.25
 
1.4%
8.14
 
1.1%
6.74
 
1.1%
4.74
 
1.1%
9.14
 
1.1%
6.24
 
1.1%
Other values (227)318
87.4%
ValueCountFrequency (%)
21
0.3%
2.41
0.3%
2.62
0.5%
2.882
0.5%
31
0.3%
ValueCountFrequency (%)
42.421
0.3%
41.91
0.3%
32.721
0.3%
28.031
0.3%
26.951
0.3%

PLT/mm3
Real number (ℝ≥0)

Distinct208
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean223.7508242
Minimum10
Maximum660
Zeros0
Zeros (%)0.0%
Memory size3.0 KiB
2021-04-26T23:46:41.224302image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile90
Q1153
median210
Q3268.25
95-th percentile414.45
Maximum660
Range650
Interquartile range (IQR)115.25

Descriptive statistics

Standard deviation99.40715299
Coefficient of variation (CV)0.4442761422
Kurtosis1.636288181
Mean223.7508242
Median Absolute Deviation (MAD)57
Skewness0.9965530766
Sum81445.3
Variance9881.782065
MonotocityNot monotonic
2021-04-26T23:46:41.435539image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15015
 
4.1%
1408
 
2.2%
1537
 
1.9%
1526
 
1.6%
1515
 
1.4%
2225
 
1.4%
2335
 
1.4%
1584
 
1.1%
1884
 
1.1%
904
 
1.1%
Other values (198)301
82.7%
ValueCountFrequency (%)
101
0.3%
241
0.3%
322
0.5%
351
0.3%
361
0.3%
ValueCountFrequency (%)
6601
0.3%
5891
0.3%
5342
0.5%
5321
0.3%
5101
0.3%

HGB
Real number (ℝ≥0)

Distinct84
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.90769231
Minimum4.2
Maximum19.6
Zeros0
Zeros (%)0.0%
Memory size3.0 KiB
2021-04-26T23:46:41.606009image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum4.2
5-th percentile8.1
Q110.6
median12.1
Q313.4
95-th percentile15.085
Maximum19.6
Range15.4
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.186685519
Coefficient of variation (CV)0.1836363808
Kurtosis0.7180330655
Mean11.90769231
Median Absolute Deviation (MAD)1.35
Skewness-0.4887965063
Sum4334.4
Variance4.781593558
MonotocityNot monotonic
2021-04-26T23:46:41.780680image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.412
 
3.3%
11.211
 
3.0%
13.111
 
3.0%
11.310
 
2.7%
12.910
 
2.7%
119
 
2.5%
13.99
 
2.5%
129
 
2.5%
11.99
 
2.5%
12.78
 
2.2%
Other values (74)266
73.1%
ValueCountFrequency (%)
4.22
0.5%
51
0.3%
5.21
0.3%
6.11
0.3%
6.92
0.5%
ValueCountFrequency (%)
19.61
 
0.3%
16.21
 
0.3%
162
0.5%
15.93
0.8%
15.63
0.8%

RBC
Real number (ℝ≥0)

Distinct195
Distinct (%)53.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.278736264
Minimum1.36
Maximum6.9
Zeros0
Zeros (%)0.0%
Memory size3.0 KiB
2021-04-26T23:46:42.011856image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1.36
5-th percentile2.8975
Q13.78
median4.335
Q34.8
95-th percentile5.547
Maximum6.9
Range5.54
Interquartile range (IQR)1.02

Descriptive statistics

Standard deviation0.8201663116
Coefficient of variation (CV)0.1916842406
Kurtosis0.7949632884
Mean4.278736264
Median Absolute Deviation (MAD)0.5
Skewness-0.3284202011
Sum1557.46
Variance0.6726727787
MonotocityNot monotonic
2021-04-26T23:46:42.214579image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.476
 
1.6%
4.16
 
1.6%
4.865
 
1.4%
4.335
 
1.4%
4.614
 
1.1%
4.124
 
1.1%
4.544
 
1.1%
4.654
 
1.1%
3.514
 
1.1%
3.964
 
1.1%
Other values (185)318
87.4%
ValueCountFrequency (%)
1.361
0.3%
1.912
0.5%
1.921
0.3%
1.961
0.3%
2.171
0.3%
ValueCountFrequency (%)
6.91
0.3%
6.61
0.3%
6.581
0.3%
6.052
0.5%
5.941
0.3%

Interactions

2021-04-26T23:44:31.943205image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:32.108619image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:32.938961image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:33.572353image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:34.080428image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:34.203280image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:34.533646image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:35.252526image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:35.963824image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:36.254333image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:36.854956image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:36.963841image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:37.587942image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:38.238365image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:38.678382image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:38.782626image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:39.215950image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:39.989389image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:40.663784image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:40.975412image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:41.624688image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:42.652887image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:43.722851image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:45.468888image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:46.890629image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:47.896772image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:49.124636image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:50.737313image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:52.448293image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:53.727391image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:55.396073image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:56.578761image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:57.676379image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:44:59.302395image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:00.873044image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:01.969878image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:03.729592image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:05.421035image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:07.100316image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:08.604277image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:10.706596image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:11.660162image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:12.521266image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:13.846198image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:15.552089image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:16.343525image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:17.317775image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:18.647593image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:19.900243image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:20.858003image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:22.145893image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:22.237490image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:22.336603image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:22.925032image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:23.501309image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:23.929336image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:24.208303image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:24.879429image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:25.455109image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:25.730550image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:26.750920image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:27.223719image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:27.692433image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:28.706807image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:29.870720image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:30.799947image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:31.327971image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:32.682952image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:33.783810image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:34.521279image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:35.577118image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:36.949771image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:38.340500image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:40.202134image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:42.111550image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:43.888144image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:45.801148image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:47.904658image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:50.270645image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:52.189548image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:54.348091image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:55.508112image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:56.570233image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:58.243596image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:45:59.864540image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:46:01.439630image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:46:02.631045image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:46:04.000147image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:46:05.792823image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:46:07.151485image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:46:08.788580image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:46:10.340406image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:46:10.835836image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:46:11.940994image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:46:13.749363image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:46:14.853231image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:46:15.449958image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:46:16.273242image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:46:17.544717image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:46:18.589903image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:46:19.590544image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:46:20.602310image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:46:21.689082image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:46:23.347748image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:46:25.065652image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:46:26.850366image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:46:27.950358image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:46:29.912264image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:46:31.754920image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T23:46:33.834661image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-04-26T23:46:42.337930image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-26T23:46:42.481787image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-26T23:46:42.619273image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-26T23:46:42.773167image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-04-26T23:46:35.420533image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-26T23:46:35.624597image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexAgeSexPCVMCVMCHMCHCRDWTLCPLT/mm3HGBRBC
0128.00.03460.11728.22011.1128.39.65.66
1241.00.044.593.128.931.0137.0241913.84.78
2340.01.041.689.528.832.2138.0932513.44.65
3476.00.036.786.626.730.814.913.4126411.34.24
4520.01.036.989.127.831.213.24.7519611.54.14
5624.00.040.193.529.631.714.513.9623312.74.29
6728.01.042.384.924.929.316.29.3321312.44.98
7814.00.043.888.12831.715.23.9222913.94.97
8916.00.038.79328.831.017.95.77211124.16
91062.00.045.686.925.329.215.610.6815113.35.25

Last rows

df_indexAgeSexPCVMCVMCHMCHCRDWTLCPLT/mm3HGBRBC
35435546.00.042.990.130.233.513.17.224414.44.76
35535625.01.037.593.732.334.517.46.731212.94
35635724.00.035.776.626.234.113.78.726612.24.66
35735828.00.030.789.728.732.0197.74579.83.42
35835922.00.029.162.620.432.6176.72009.54.65
35936017.00.039.280.727.734.413.44.418013.54.86
36036151.00.035.291.73133.813.35.6215154.85
36136221.01.039.788.729.333.013.59.232913.14.47
36236335.01.036.286.727.932.113.56.4817413.24.75
36336426.00.044.489.730.634.212.38.827915.24.95